Ivo Vondrák

A Description of a Highly Modular System for the Emergent Flood Prediction

The main goal of our system is to provide the end user with information about an approaching disaster. The concept is to ensure information access to adequate data for all potential users, including citizens, local mayors, governments, and specialists, within one system. It is obvious that there is a knowledge gap between the lay user and specialist. Therefore, the system must be able to provide this information in a simple format for the less informed user while providing more complete information with computation adjustment and parameterization options to more qualified users. Important feature is the open structure and modular architecture that enables the usage of different modules. Modules can contain different functions, alternative simulations or additional features. Since the architectural structure is open, modules can be combined in any way to achieve any desired function in the system. One of many important modules is our own analytic solution to the flood waves for a small basin to our system.

Multiple Scenarios Computing In The Flood Prediction System FLOREON.

Floods are the most frequent natural disasters affecting the Moravian-Silesian region. Therefore a system that could predict flood extents and help in the operative disaster management was requested. The FLOREON system was created to fulfil these requests. This article describes utilization of HPC (high performance computing) in running multiple hydrometeorological simulations concurrently in the FLOREON system that should predict upcoming floods and warn against them. These predictions are based on the data inputs from NWFS (numerical weather forecast systems) (e.g. ALADIN) that are then used to run the rainfall-runoff and hydrodynamic models. Preliminary results of these experiments are presented in this article.

Building process definition with ontology background

Documented software processes and their assessments are the basics of modern software development. Currently the semantic web, knowledge bases and knowledge management have many applications. Yet, applications to support software processes (and business processes in general) are rather neglected. In this paper we focus on applying a knowledge layer into software processes and on the design of such a knowledge base. After a brief description of some classical fundamental approaches to software processes and knowledge bases, we propose an improvement based on the application of a machine readable knowledge base. In particular, we aim at optimizing and improving software process development using knowledge bases created with the aim to formally describe software process development.

Modeling and Assessment in IT Service Process Improvement

This paper is based on the experiences of a research project with the aim to develop modeling and assessment readiness for IT companies. As a part of the project, process assessments for process improvement purposes were performed in some of the participating companies. This paper describes the background of applying process reference model based assessment and modeling of the processes for the same process instance. Some findings and experiences based on an industry case are documented. We also discuss how these approaches could be combined in an efficient way.

Time-Dependent Route Planning for the Highways in the Czech Republic

This paper presents an algorithm for dynamic travel time computation along Czech Republic highways. The dynamism is represented by speed profiles used for computation of travel times at specified time. These speed profiles have not only the information about an optimal speed, but also a probability of this optimal speed and the probability of the speed which represents the possibility of traffic incident occurrence. Thus, the paper is focused on the analysis of paths with the uncertainty created by traffic incidents. The result of the algorithm is the probability distribution of travel times on a selected path. Based on these results, it is possible to plan a departure time with the best mean travel time for routes along the Czech Republic highways for a specified maximal acceptable travel time. This method will be a part of a larger algorithm for dynamic traffic routing.

Efficient searching in large inheritance hierarchies

Inheritance hierarchies have become more and more complex according to an enlargement of object-oriented technology. One of the main problems is the effective searching in such hierarchies. More sophisticated algorithms are needed to searching in the data. In this article we present a novel approach to efficient searching in large inheritance hierarchies. The updatable approach employs the multi-dimensional data structures to indexing inheritance hierarchies and effective searching in the data.

Effective clustering algorithm for high-dimensional sparse data based on SOM

With increasing opportunities for analyzing large data sources, we have noticed a lack of effective processing in datamining tasks working with large sparse datasets of high dimensions. This work focuses on this issue and on effective clustering using models of artificial intelligence. The authors of this article propose an effective clustering algorithm to exploit the features of neural networks, and especially Self Organizing Maps (SOM), for the reduction of data dimensionality. The issue of computational complexity is resolved by using a parallelization of the standard SOM algorithm. The authors have focused on the acceleration of the presented algorithm using a version suitable for data collections with a certain level of sparsity. Effective acceleration is achieved by improving the winning neuron finding phase and the weight actualization phase. The output presented here demonstrates sufficient acceleration of the standard SOM algorithm while preserving the appropriate accuracy.

Parallel Hybrid SOM Learning on High Dimensional Sparse Data

Self organizing maps (also called Kohonen maps) are known for their capability of projecting high-dimensional space into lower dimensions. There are commonly discussed problems like rapidly increased computational complexity or specific similarity representation in the high-dimensional space. In the paper there is proposed the effective clustering algorithm based on self organizing map with the main purpose to reduce high dimension of the input dataset. The problem of computational complexity is solved using parallelization; the speed of proposed algorithm is accelerated using the algorithm version suitable for data collections with certain level of sparsity.

FLOREON – System For Flood Prediction

The main goal of our system is to provide the end user with information about an approaching disaster. The concept is to ensure information access to adequate data for all potential users, including citizens, local mayors, governments, and specialists, within one system. It is obvious that there is a knowledge gap between the lay user and specialist. Therefore, the system must be able to provide this information in a simple format for the less informed user while providing more complete information with computation adjustment and parameterization options to more qualified users. One system feature in high demand is the ability to display reliable and understandable graphical and textual information. Information for various types of users must be adapted to a desired format which is understandable to a particular group of people. For example, a specialist can ask for all available results from different simulation models in text format. This type of information may be useless, however, to the user who only wants to find out whether or not his house will be flooded. Another important feature is the open structure and modular architecture that enables the usage of different modules. Modules can contain different functions, alternative simulations or additional features. Since the architectural structure is open, modules can be combined in any way to achieve any desired function in the system.

Using Kohonen Maps and Singular Value Decomposition for Plagiarism Detection

Plagiarism has become one area of interest for re-searchers due to its importance, and its fast growing rates. Effective clustering methods and faster search tools for matching and discovering the similarities between documents were the main two areas for the researchers. Many tools and techniques have been developed for plagiarism detection. In this paper we use singular value decomposition for its effective clustering of the documents in-order to reduce search time by creating a new matrix with fewer dimensions used for clustering the original (source) documents, and we use Neural Networks for local matching and comparison between a suspicious document and a source document, Kohonen maps (Self-organizing maps (SOM)) used to visualized and comparison of the result, in which represent the result as picture that easier to be analyzed.